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The epic Covid sickness 2019 (COVID-19) has turned into the significant danger to humankind in year 2020. The pandemic COVID-19 flare-up has influenced more than 2.7 million individuals and caused around 187 thousand fatalities worldwide [1] inside scarcely any months of its first appearance in Wuhan city of China and the number is developing quickly in various pieces of world. As researcher everywhere on the world are battling to discover the fix and treatment for COVID-19, the urgent advance fighting against COVID-19 is the screening of immense number of associated cases for disconnection and isolate with the patients. One of the key methodologies in screening of COVID-19 can be chest radiological imaging. The early investigations on the patients influenced by COVID-19 shows the attributes variations from the norm in chest radiography pictures. This introduced a chance to utilize distinctive counterfeit clever (AI) frameworks dependent on profound picking up utilizing chest radiology pictures for the recognition of COVID-19 and numerous such framework were proposed indicating promising outcomes. In this paper, we proposed a profound learning based convolution neural organization to characterize COVID-19, Pneumonia and Normal cases from chest radiology pictures. The proposed convolution neural organization (CNN) grouping model had the option to accomplish exactness of 94.85% on test dataset. The trial was completed utilizing the subset of information accessible in GitHub and Kaggle.

Breast Cancer is formed by an abnormal development of cells in breast. The cells of body separate in an incessant method and occupy to surrounding tissues. It is the important reason of death amongst women and after lung cancer breast cancer is second cause of women deaths. Early breast cancer detection can lead to death rate decrease. The mammography is executed to discover the breast cancer tumor at earlier stages. Early breast cancer tumors detection based on the both the radiologists capability to read mammogram images and image quality. The tumors classification is a medical application that set a huge issue for in the breast cancer recognition area. Therefore, in this paper, a multiple otsu's thresholding method is presented with Mutlti-class SVM (M-SVM) classifier to enhance the tumor classification in mammogram images for cancer tumor detection. In this process, elimination of artifacts, noise and surplus parts that are presented in mammogram images by employing preprocessing tasks and after that it improves the mammogram image contrast utilizing CLAHE (Contrast Limited Adaptive Histrogram Equalization) technique for simpler recognition of tumors in breast. We segment the images using Multiple Otsu's thresholding technique to identify the region of interest in mammogram image after preprocessing and image enhancement. The GLDM (Gray Level Difference Method) is exploited to extract the features from the mammogram image. Feature extraction has been employed to with hindsight examine screening mammograms in use prior to the malignant mass discovery for early breast cancer tumor detection. The extracted features can be given to the M-SVM Classifier to classify the tumor in mammogram image into malignant, benign or normal based on the features. The classification accurateness based on the stage of feature extraction. Results of mammogram image is planned by classification and lastly image categorized into Normal, malignant or Benign. Experimental results of proposed method can show that this presented technique executes well with the accurateness of classification reaching almost 84% in evaluation with existing algorithms.

The satellite imagery classification task is fundamental to spatial knowledge discovery. Land Cover and Land usage (LULC) maps are created using a variety of image classification techniques, making it easier to conduct research on spatial and ecological processes as well as human activities. One of the most well-known applications of geographical monitoring is LULC classification. Owing to its improved feature learning and feature expression capacity, the convolutional neural network (CNN) has made several breakthroughs in feature extraction as well as classification of multispectral images in recent times as compared with conventional machine learning approaches. But on the other hand, standard CNN models have certain disadvantages, for instance, a large number of layers, which contribute to difficult computing costs. The Hybrid Enriched Stacked Auto Encoder and Pre-Activated Residual Convolutional Neural Network combined with a Fruit Fly Optimization Algorithm (HESAE-FFO-MPARCNN) has formulated where FFO used to optimise parameters and thus enhance the accuracy of classification in this work to tackle this issue. The designed FFO-MPARCNN model with its modified hyperparameters produces higher classical models as PB-RNN, ResNet and FHS-DBN for computational efficiency and accuracy of classification.